Machine Learning Function Approximation: This code implements the fully-connected Deep Neural Network (DNN) architectures considered in the paper "The gap between theory and practice in function approximation with deep neural networks" available at https://arxiv.org/abs/2001.07523
Multi-Head ReLU Implicit Neural Representation Networks
Code for optimizing MILP problems with embedded ReLU networks
Implementing a 4 layer neural to identify digits from a 28x28 grid using just python and numpy
Text Generation
Traininig a simple neural network with ReLU activation function to classify the data.
This project utilizes neural networks to recognize handwritten digits (0-9) through multiclass classification, employing ReLU activation and Softmax function for accurate predictions.
Computer Vision Model for Age Estimation from Facial Images
This repository delves into the role of activation functions in perceptron-based classification models. It features a comprehensive Jupyter notebook demonstrating different activation functions, their mathematical foundations, and their impact on model performance.
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